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- W3166161745 abstract "We present a graph neural network to learn graph coloring heuristics using reinforcement learning. Our learned deterministic heuristics give better solutions than classical degree-based greedy heuristics and only take seconds to evaluate on graphs with tens of thousands of vertices. As our approach is based on policy-gradients, it also learns a probabilistic policy as well. These probabilistic policies outperform all greedy coloring baselines and a machine learning baseline. Our approach generalizes several previous machine-learning frameworks, which applied to problems like minimum vertex cover. We also demonstrate that our approach outperforms two greedy heuristics on minimum vertex cover." @default.
- W3166161745 created "2021-06-22" @default.
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- W3166161745 date "2021-06-07" @default.
- W3166161745 modified "2023-09-27" @default.
- W3166161745 title "Learning Combinatorial Node Labeling Algorithms." @default.
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